chapter 4

ch04/README.md

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+Python Machine Learning - Code Examples
+
+
+##  Chapter 4: Building Good Training Sets – Data Preprocessing
+
+### Chapter Outline
+
+- Dealing with missing data
+  - Identifying missing values in tabular data
+  - Eliminating training examples or features with missing values
+  - Imputing missing values
+  - Understanding the scikit-learn estimator API
+- Handling categorical data
+  - Nominal and ordinal features
+  - Creating an example dataset
+  - Mapping ordinal features
+  - Encoding class labels
+  - Performing one-hot encoding on nominal features
+- Partitioning a dataset into separate training and test sets
+- Bringing features onto the same scale
+- Selecting meaningful features
+  - L1 and L2 regularization as penalties against model complexity
+  - A geometric interpretation of L2 regularization
+  - Sparse solutions with L1 regularization
+  - Sequential feature selection algorithms
+- Assessing feature importance with random forests
+- Summary
+
+### A note on using the code examples
+
+The recommended way to interact with the code examples in this book is via Jupyter Notebook (the `.ipynb` files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.
+
+![](../ch02/images/jupyter-example-1.png)
+
+
+
+Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:
+
+    conda install jupyter notebook
+
+Then you can launch jupyter notebook by executing
+
+    jupyter notebook
+
+A window will open up in your browser, which you can then use to navigate to the target directory that contains the `.ipynb` file you wish to open.
+
+**More installation and setup instructions can be found in the [README.md file of Chapter 1](../ch01/README.md)**.
+
+**(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: [`ch04.ipynb`](ch04.ipynb))**
+
+In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.
+
+![](../ch02/images/jupyter-example-2.png)
+
+
+When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (`.py` files) that can be viewed and edited in any plaintext editor.